Data dispersion: Now you see it. now you don't

Sellers, K F and Shmueli, G (2013) Data dispersion: Now you see it. now you don't. Communications in Statistics - Theory and Methods, 42 (17). pp. 3134-3137.

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Abstract

Poisson regression is the most well-known method for modeling count data. When data display over-dispersion, thereby violating the underlying equi-dispersion assumption of Poisson regression, the common solution is to use negative-binomial regression. We show, however, that count data that appear to be equi-or over-dispersed may actually stem from a mixture of populations with different dispersion levels. To detect and model such a mixture, we introduce a generalization of the Conway-Maxwell-Poisson (COM-Poisson) regression model that allows for group-level dispersion. We illustrate mixed dispersion effects and the proposed methodology via semi-authentic data. © 2013 Taylor and Francis Group, LLC.

ISB Creators:
ISB CreatorsORCiD
Shmueli, GUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Apparent dispersion; Conway-Maxwell-Poisson (COM-Poisson) regression; Mixture model; Over-dispersion; Under-dispersion
Subjects: Applied Statistics and Computing
Depositing User: Users 13 not found.
Date Deposited: 16 Nov 2014 11:14
Last Modified: 17 Nov 2014 05:03
URI: http://eprints.exchange.isb.edu/id/eprint/283
Publisher URL: http://dx.doi.org/10.1080/03610926.2011.621575
Publisher OA policy: http://www.sherpa.ac.uk/romeo/issn/0361-0926/
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